{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pickle as pkl\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from flip_gradient import flip_gradient\n",
    "from utils import *\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "# Process MNIST\n",
    "mnist_train = (mnist.train.images > 0).reshape(55000, 28, 28, 1).astype(np.uint8) * 255\n",
    "mnist_train = np.concatenate([mnist_train, mnist_train, mnist_train], 3)\n",
    "mnist_test = (mnist.test.images > 0).reshape(10000, 28, 28, 1).astype(np.uint8) * 255\n",
    "mnist_test = np.concatenate([mnist_test, mnist_test, mnist_test], 3)\n",
    "\n",
    "# Load MNIST-M\n",
    "mnistm = pkl.load(open('mnistm_data.pkl', 'rb'))\n",
    "mnistm_train = mnistm['train']\n",
    "mnistm_test = mnistm['test']\n",
    "mnistm_valid = mnistm['valid']\n",
    "\n",
    "# Compute pixel mean for normalizing data\n",
    "pixel_mean = np.vstack([mnist_train, mnistm_train]).mean((0, 1, 2))\n",
    "\n",
    "# Create a mixed dataset for TSNE visualization\n",
    "num_test = 500\n",
    "combined_test_imgs = np.vstack([mnist_test[:num_test], mnistm_test[:num_test]])\n",
    "combined_test_labels = np.vstack([mnist.test.labels[:num_test], mnist.test.labels[:num_test]])\n",
    "combined_test_domain = np.vstack([np.tile([1., 0.], [num_test, 1]),\n",
    "        np.tile([0., 1.], [num_test, 1])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88827e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88827e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow_grid(mnist_train)\n",
    "imshow_grid(mnistm_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "class MNISTModel(object):\n",
    "    \"\"\"Simple MNIST domain adaptation model.\"\"\"\n",
    "    def __init__(self):\n",
    "        self._build_model()\n",
    "    \n",
    "    def _build_model(self):\n",
    "        \n",
    "        self.X = tf.placeholder(tf.uint8, [None, 28, 28, 3])\n",
    "        self.y = tf.placeholder(tf.float32, [None, 10])\n",
    "        self.domain = tf.placeholder(tf.float32, [None, 2])\n",
    "        self.l = tf.placeholder(tf.float32, [])\n",
    "        self.train = tf.placeholder(tf.bool, [])\n",
    "        \n",
    "        X_input = (tf.cast(self.X, tf.float32) - pixel_mean) / 255.\n",
    "        \n",
    "        # CNN model for feature extraction\n",
    "        with tf.variable_scope('feature_extractor'):\n",
    "\n",
    "            W_conv0 = weight_variable([5, 5, 3, 32])\n",
    "            b_conv0 = bias_variable([32])\n",
    "            h_conv0 = tf.nn.relu(conv2d(X_input, W_conv0) + b_conv0)\n",
    "            h_pool0 = max_pool_2x2(h_conv0)\n",
    "            \n",
    "            W_conv1 = weight_variable([5, 5, 32, 48])\n",
    "            b_conv1 = bias_variable([48])\n",
    "            h_conv1 = tf.nn.relu(conv2d(h_pool0, W_conv1) + b_conv1)\n",
    "            h_pool1 = max_pool_2x2(h_conv1)\n",
    "            \n",
    "            # The domain-invariant feature\n",
    "            self.feature = tf.reshape(h_pool1, [-1, 7*7*48])\n",
    "            \n",
    "        # MLP for class prediction\n",
    "        with tf.variable_scope('label_predictor'):\n",
    "            \n",
    "            # Switches to route target examples (second half of batch) differently\n",
    "            # depending on train or test mode.\n",
    "            all_features = lambda: self.feature\n",
    "            source_features = lambda: tf.slice(self.feature, [0, 0], [batch_size // 2, -1])\n",
    "            classify_feats = tf.cond(self.train, source_features, all_features)\n",
    "            \n",
    "            all_labels = lambda: self.y\n",
    "            source_labels = lambda: tf.slice(self.y, [0, 0], [batch_size // 2, -1])\n",
    "            self.classify_labels = tf.cond(self.train, source_labels, all_labels)\n",
    "            \n",
    "            W_fc0 = weight_variable([7 * 7 * 48, 100])\n",
    "            b_fc0 = bias_variable([100])\n",
    "            h_fc0 = tf.nn.relu(tf.matmul(classify_feats, W_fc0) + b_fc0)\n",
    "\n",
    "            W_fc1 = weight_variable([100, 100])\n",
    "            b_fc1 = bias_variable([100])\n",
    "            h_fc1 = tf.nn.relu(tf.matmul(h_fc0, W_fc1) + b_fc1)\n",
    "\n",
    "            W_fc2 = weight_variable([100, 10])\n",
    "            b_fc2 = bias_variable([10])\n",
    "            logits = tf.matmul(h_fc1, W_fc2) + b_fc2\n",
    "            \n",
    "            self.pred = tf.nn.softmax(logits)\n",
    "            self.pred_loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self.classify_labels)\n",
    "\n",
    "        # Small MLP for domain prediction with adversarial loss\n",
    "        with tf.variable_scope('domain_predictor'):\n",
    "            \n",
    "            # Flip the gradient when backpropagating through this operation\n",
    "            feat = flip_gradient(self.feature, self.l)\n",
    "            \n",
    "            d_W_fc0 = weight_variable([7 * 7 * 48, 100])\n",
    "            d_b_fc0 = bias_variable([100])\n",
    "            d_h_fc0 = tf.nn.relu(tf.matmul(feat, d_W_fc0) + d_b_fc0)\n",
    "            \n",
    "            d_W_fc1 = weight_variable([100, 2])\n",
    "            d_b_fc1 = bias_variable([2])\n",
    "            d_logits = tf.matmul(d_h_fc0, d_W_fc1) + d_b_fc1\n",
    "            \n",
    "            self.domain_pred = tf.nn.softmax(d_logits)\n",
    "            self.domain_loss = tf.nn.softmax_cross_entropy_with_logits(logits=d_logits, labels=self.domain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model graph\n",
    "graph = tf.get_default_graph()\n",
    "with graph.as_default():\n",
    "    model = MNISTModel()\n",
    "    \n",
    "    learning_rate = tf.placeholder(tf.float32, [])\n",
    "    \n",
    "    pred_loss = tf.reduce_mean(model.pred_loss)\n",
    "    domain_loss = tf.reduce_mean(model.domain_loss)\n",
    "    total_loss = pred_loss + domain_loss\n",
    "\n",
    "    regular_train_op = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(pred_loss)\n",
    "    dann_train_op = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(total_loss)\n",
    "    \n",
    "    # Evaluation\n",
    "    correct_label_pred = tf.equal(tf.argmax(model.classify_labels, 1), tf.argmax(model.pred, 1))\n",
    "    label_acc = tf.reduce_mean(tf.cast(correct_label_pred, tf.float32))\n",
    "    correct_domain_pred = tf.equal(tf.argmax(model.domain, 1), tf.argmax(model.domain_pred, 1))\n",
    "    domain_acc = tf.reduce_mean(tf.cast(correct_domain_pred, tf.float32))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Source only training\n",
      "Source (MNIST) accuracy: 0.9873\n",
      "Target (MNIST-M) accuracy: 0.5342\n",
      "\n",
      "Domain adaptation training\n",
      "Source (MNIST) accuracy: 0.9781\n",
      "Target (MNIST-M) accuracy: 0.7076\n",
      "Domain accuracy: 0.68\n"
     ]
    }
   ],
   "source": [
    "def train_and_evaluate(training_mode, graph, model, num_steps=8600, verbose=False):\n",
    "    \"\"\"Helper to run the model with different training modes.\"\"\"\n",
    "\n",
    "    with tf.Session(graph=graph) as sess:\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        # Batch generators\n",
    "        gen_source_batch = batch_generator(\n",
    "            [mnist_train, mnist.train.labels], batch_size // 2)\n",
    "        gen_target_batch = batch_generator(\n",
    "            [mnistm_train, mnist.train.labels], batch_size // 2)\n",
    "        gen_source_only_batch = batch_generator(\n",
    "            [mnist_train, mnist.train.labels], batch_size)\n",
    "        gen_target_only_batch = batch_generator(\n",
    "            [mnistm_train, mnist.train.labels], batch_size)\n",
    "\n",
    "        domain_labels = np.vstack([np.tile([1., 0.], [batch_size // 2, 1]),\n",
    "                                   np.tile([0., 1.], [batch_size // 2, 1])])\n",
    "\n",
    "        # Training loop\n",
    "        for i in range(num_steps):\n",
    "            \n",
    "            # Adaptation param and learning rate schedule as described in the paper\n",
    "            p = float(i) / num_steps\n",
    "            l = 2. / (1. + np.exp(-10. * p)) - 1\n",
    "            lr = 0.01 / (1. + 10 * p)**0.75\n",
    "\n",
    "            # Training step\n",
    "            if training_mode == 'dann':\n",
    "\n",
    "                X0, y0 = next(gen_source_batch)\n",
    "                X1, y1 = next(gen_target_batch)\n",
    "                X = np.vstack([X0, X1])\n",
    "                y = np.vstack([y0, y1])\n",
    "\n",
    "                _, batch_loss, dloss, ploss, d_acc, p_acc = sess.run(\n",
    "                    [dann_train_op, total_loss, domain_loss, pred_loss, domain_acc, label_acc],\n",
    "                    feed_dict={model.X: X, model.y: y, model.domain: domain_labels,\n",
    "                               model.train: True, model.l: l, learning_rate: lr})\n",
    "\n",
    "                if verbose and i % 100 == 0:\n",
    "                    print('loss: {}  d_acc: {}  p_acc: {}  p: {}  l: {}  lr: {}'.format(\n",
    "                            batch_loss, d_acc, p_acc, p, l, lr))\n",
    "\n",
    "            elif training_mode == 'source':\n",
    "                X, y = next(gen_source_only_batch)\n",
    "                _, batch_loss = sess.run([regular_train_op, pred_loss],\n",
    "                                     feed_dict={model.X: X, model.y: y, model.train: False,\n",
    "                                                model.l: l, learning_rate: lr})\n",
    "\n",
    "            elif training_mode == 'target':\n",
    "                X, y = next(gen_target_only_batch)\n",
    "                _, batch_loss = sess.run([regular_train_op, pred_loss],\n",
    "                                     feed_dict={model.X: X, model.y: y, model.train: False,\n",
    "                                                model.l: l, learning_rate: lr})\n",
    "\n",
    "        # Compute final evaluation on test data\n",
    "        source_acc = sess.run(label_acc,\n",
    "                            feed_dict={model.X: mnist_test, model.y: mnist.test.labels,\n",
    "                                       model.train: False})\n",
    "\n",
    "        target_acc = sess.run(label_acc,\n",
    "                            feed_dict={model.X: mnistm_test, model.y: mnist.test.labels,\n",
    "                                       model.train: False})\n",
    "        \n",
    "        test_domain_acc = sess.run(domain_acc,\n",
    "                            feed_dict={model.X: combined_test_imgs,\n",
    "                                       model.domain: combined_test_domain, model.l: 1.0})\n",
    "        \n",
    "        test_emb = sess.run(model.feature, feed_dict={model.X: combined_test_imgs})\n",
    "        \n",
    "    return source_acc, target_acc, test_domain_acc, test_emb\n",
    "\n",
    "\n",
    "print('\\nSource only training')\n",
    "source_acc, target_acc, _, source_only_emb = train_and_evaluate('source', graph, model)\n",
    "print('Source (MNIST) accuracy:', source_acc)\n",
    "print('Target (MNIST-M) accuracy:', target_acc)\n",
    "\n",
    "print('\\nDomain adaptation training')\n",
    "source_acc, target_acc, d_acc, dann_emb = train_and_evaluate('dann', graph, model)\n",
    "print('Source (MNIST) accuracy:', source_acc)\n",
    "print('Target (MNIST-M) accuracy:', target_acc)\n",
    "print('Domain accuracy:', d_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc892b0e690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc80ba65a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=3000)\n",
    "source_only_tsne = tsne.fit_transform(source_only_emb)\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=3000)\n",
    "dann_tsne = tsne.fit_transform(dann_emb)\n",
    "        \n",
    "plot_embedding(source_only_tsne, combined_test_labels.argmax(1), combined_test_domain.argmax(1), 'Source only')\n",
    "plot_embedding(dann_tsne, combined_test_labels.argmax(1), combined_test_domain.argmax(1), 'Domain Adaptation')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
